For this exercise, we will use the same subset of the GESIS Panel Special Survey on the Coronavirus SARS-CoV-2 Outbreak in Germany data as in the presentation. Just run the following code to go through the wrangling pipeline. Remember that the .csv file should be stored in the data folder in the directory with the course materials.

library(tidyverse)
library(naniar)

gesis_panel_corona <- read_csv2("../data/ZA5667_v1-1-0.csv")
missings <- c(-111, -99, -77, -33, -22)

corona_survey <- gesis_panel_corona %>% 
  select(id,
         sex:education_cat,
         choice_of_party,
         left_right = political_orientation,
         risk_self =  hzcy001a,
         risk_surround =  hzcy002a,
         avoid_places =  hzcy006a,
         keep_distance =  hzcy007a,
         wash_hands = hzcy011a,
         stockup_supplies =  hzcy013a,
         reduce_contacts =  hzcy014a,
         wear_mask = hzcy015a,
         trust_rki = hzcy047a,
         trust_government = hzcy048a,
         trust_chancellor = hzcy049a,
         trust_who = hzcy051a,
         trust_scientists = hzcy052a,
         info_national_public_tv = hzcy084a,
         info_national_newspaper = hzcy086a,
         info_local_newspaper = hzcy089a,
         info_facebook = hzcy090a,
         info_other_social_media = hzcy091a) %>% 
  replace_with_na_all(condition = ~.x %in% missings) %>% 
    replace_with_na(replace = list(choice_of_party = c(97,98),
                                   risk_self = c(97),
                                   risk_surround = c(97),
                                   trust_rki = c(98),
                                   trust_government = c(98),
                                   trust_chancellor = c(98),
                                   trust_who = c(98),
                                   trust_scientists = c(98))) %>%
  mutate(sex = recode_factor(sex,
                               `1`= "Male",
                               `2` = "Female"),
           education_cat = recode_factor(education_cat,
                                       `1` = "Low",
                                       `2` = "Medium",
                                       `3`= "High",
                                       .ordered = TRUE),
           age_cat = recode_factor(age_cat,
                                   `1`= "<= 25 years",
                                   `2`= "26 to 30 years",
                                   `3` = "31 to 35 years",
                                   `4` = "36 to 40 years",
                                   `5` = "41 to 45 years",
                                   `6` = "46 to 50 years",
                                   `7` = "51 to 60 years",
                                   `8` = "61 to 65 years",
                                   `9`= "66 to 70 years",
                                   `10` = ">= 71 years",
                                   .ordered = TRUE),
           choice_of_party = recode_factor(choice_of_party,
                                           `1`= "CDU/CSU",
                                           `2`= "SPD",
                                           `3` = "FDP",
                                           `4` = "Linke",
                                           `5` = "Gruene",
                                           `6` = "AfD",
                                           `7` = "Other")
    ) %>% 
  mutate(sum_measures = avoid_places + 
           keep_distance + 
           wash_hands + 
           stockup_supplies + 
           reduce_contacts + 
           wear_mask,
         sum_sources = info_national_public_tv + 
           info_national_newspaper + 
           info_local_newspaper + 
           info_facebook + 
           info_other_social_media) %>% 
  rowwise() %>% 
  mutate(mean_trust = mean(c(trust_rki, 
                             trust_government, 
                             trust_chancellor, 
                             trust_who, 
                             trust_scientists),
                           na.rm = TRUE)) %>% 
  ungroup()

As we will use the same dataset again in the next exercise in this session, it makes sense to save it. To preserve the information about the variable types, it is best to save it as a .rds file. You can do this with the following command:

saveRDS(corona_survey, "../data/gp_corona_subset.rds")

In case you have not done so, please also install the summarytools and the GGally package. The following code chunk will check if you have these packages installed and install them, if that is not the case.

if (!require(summaryrtools)) install.packages("summarytools")
if (!require(summaryrtools)) install.packages("GGally")

1

Print a simple table with the frequencies of the variable education_cat. Also include the counts for missing values.

You can use the table() function from base R for this.

2

Use a function from the summarytools package to get summary statistics for the following variables in your dataset: left_right, sum_measures, mean_trust.

You need to combine a wrangling function from the dplyr package with descr() from summarytools.

3

Use another function from summarytools to display the counts and frequencies for the categories in the age_cat variable.

The function you need is freq()

4

Use yet another summarytools function to create a crosstable for the variables sex and education_cat.

The function for this is ctable().

5

Use the correlation package to calculate and print correlations between the following variables: risk_self, risk_surround, sum_measures, sum_sources

You need to use select() from dplyr and correlation() from the package with the same name.

6

As a final task in this exercise on EDA, plot the above correlations with a function from the GGally package. The plot should include the coefficients rounded to two decimal places as labels.

The required function is ggcorr().